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Python for Finance

You're reading from Python for Finance Apply powerful finance models and quantitative analysis with Python

Product type Paperback

Published in Jun 2017

Publisher

ISBN-13 9781787125698

Length 586 pages

Edition 2nd Edition

Languages

Python

Tools

Matplotlib

Concepts

Data Analysis

Author (1):

Yuxing Yan

View More author details

Table of Contents (17) Chapters

Preface

1. Python Basics FREE CHAPTER

2. Introduction to Python Modules

3. Time Value of Money

4. Sources of Data

Diving into deeper concepts

5. Bond and Stock Valuation

6. Capital Asset Pricing Model

7. Multifactor Models and Performance Measures

8. Time-Series Analysis

9. Portfolio Theory

10. Options and Futures

11. Value at Risk

12. Monte Carlo Simulation

13. Credit Risk Analysis

14. Exotic Options

15. Volatility, Implied Volatility, ARCH, and GARCH

Index

Introduction to matplotlib

Graphs and other visual representations have become more important in explaining many complex financial concepts, trading strategies, and formulas.

In this section, we discuss the matplotlib module, which is used to create various types of graphs. In addition, the module will be used intensively in Chapter 10, Options and Futures, when we discuss the famous Black-Scholes-Merton option model and various trading strategies. The matplotlib module is designed to produce publication-quality figures and graphs. The matplotlib module depends on NumPy and SciPy, which were discussed in the previous sections. To save generated graphs, there are several output formats available, such as PDF, Postscript, SVG, and PNG.

How to install matplotlib

If Python was installed by using the Anaconda super package, then matplotlib is preinstalled already. After launching Spyder, type the following line to test. If there is no error, it means that we have imported/uploaded the module successfully...

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